A Service-Oriented Composition Framework with QoS Management
Bibliographic record
Abstract
Quality of Services (QoS) management in compositions of services requires careful consideration of QoS characteristics of the services and effective QoS management in their execution. This paper describes an approach to implementation of QoS management in compositions of Web services in the context of Computational Quality Attributes and Service Level Agreements. Building on prior research work of others in the use of Message Detail Records, this paper integrates the results from several research threads to propose a QoS Management Architecture to support dynamic processing of service- and flow level quality attributes to support QoS requests and analyses in Web-service-oriented architectures. The study of QoS management in a Web service composition framework was motivated by the evolution of the composition framework for a toolkit for integration and experimentation of distributed system applications. A message tracking model is proposed for supporting QoS end-to-end management by applying the Computational Quality Attribute (CQA) concepts of Flow-Service-Quality engineering. Quality attributes are defined, computed and acted upon as dynamic characteristics of systems, with values constantly changing in operation. A CQA provision is illustrated, with a simple Web Services travel reservation example. The example is elaborated to illustrate QoS end-to-end management using the Simple Object Access Protocol (SOAP) message tracking model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".